Attention IoU: Examining Biases in CelebA using Attention Maps
Aaron Serianni, Tyler Zhu, Olga Russakovsky and Vikram V. Ramaswamy
Computer Vision and Pattern Recognition (CVPR), 2025.
Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations
Indu Panigrahi, Sunnie S. Y. Kim*, Amna Liaqat*, Rohan Jinturkar, Olga Russakovsky, Ruth Fong and Parastoo Abtahi
(* = equal contribution)
ACM Conference on Human Factors in Computing Systems (CHI), Extended Abstract Track, 2025.
D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
Yongqi Yang, Zhihao Qian, Ye Zhu, Olga Russakovsky and Yu Wu
Computer Vision and Pattern Recognition (CVPR), 2025.
Portraying Large Language Models as Machines, Tools, or Companions Affects What Mental Capacities Humans Attribute to Them
Allison Chen, Sunnie S. Y. Kim, Amaya Dharmasiri, Olga Russakovsky and Judith E. Fan
ACM Conference on Human Factors in Computing Systems (CHI), Extended Abstract Track, 2025.
Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo and Olga Russakovsky
ACM Conference on Human Factors in Computing Systems (CHI), 2025.
Unifying Specialized Visual Encoders for Video Language Models
Jihoon Chung*, Tyler Zhu*, Max Gonzalez Saez-Diez, Juan Carlos Niebles, Honglu Zhou and Olga Russakovsky
arXiv preprint arXiv:2501.01426, 2025.
[paper] [code] [website] [bibtex]
ICONS: Influence Consensus for Vision-Language Data Selection
Xindi Wu, Mengzhou Xia, Rulin Shao, Zhiwei Deng, Pang Wei Koh and Olga Russakovsky
arXiv preprint arXiv:2501.00654, 2024.
[paper] [code] [website] [bibtex]
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty
Xindi Wu*, Dingli Yu*, Yangsibo Huang*, Olga Russakovsky and Sanjeev Arora
Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2024.
[paper] [code] [website] [bibtex]
Benchmark Suites Instead of Leaderboards for Evaluating AI Fairness
Angelina Wang, Aaron Hertzmann and Olga Russakovsky
Patterns, 2024.
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng and Olga Russakovsky
International Conference on Machine Learning (ICML), 2024.
Analyzing the Roles of Language and Vision in Learning from Limited Data
Allison Chen, Ilia Sucholutsky, Olga Russakovsky and Tom Griffiths
Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2024.
ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithms
William Yang, Byron Zhang and Olga Russakovsky
International Conference on Learning Representations (ICLR), 2024.
Vision-Language Dataset Distillation
Xindi Wu, Byron Zhang, Zhiwei Deng and Olga Russakovsky
Transactions on Machine Learning Research (TMLR), 2024.
Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning
Nobline Yoo and Olga Russakovsky
International Conference on Computer Vision (ICCVW) ROAD++ Workshop, 2023.
Boundary Guided Learning-Free Semantic Control with Diffusion Models
Ye Zhu, Yu Wu, Zhiwei Deng, Olga Russakovsky and Yan Yan
Neural Information Processing Systems (NeurIPS), 2023.
[paper] [code] [website] [bibtex]
GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognition
Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron B. Adcock, Laurens van der Maaten, Deepti Ghadiyaram and Olga Russakovsky
Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2023.
[paper] [code] [website] [bibtex]
Overwriting Pretrained Bias with Finetuning Data
Angelina Wang and Olga Russakovsky
International Conference on Computer Vision (ICCV), 2023.
Gender Artifacts in Visual Datasets
Nicole Meister*, Dora Zhao*, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong and Olga Russakovsky
(* = equal contribution)
International Conference on Computer Vision (ICCV), 2023.
Art and the Science of Generative AI
Ziv Epstein, Aaron Hertzmann, the Investigators of Human Creativity (Memo Akten, Hany Farid, Jessica Fjeld, Morgan R. Frank, Matthew Groh, Laura Herman, Neil Leach, Robert Mahari, Alex Pentland, Olga Russakovsky, Hope Schroeder and Amy Smith)
Science Perspectives, 2023.
[paper] [extended white paper] [bibtex]
Discrete Diffusion Reward Guidance Methods for Offline Reinforcement Learning
Matthew Coleman, Olga Russakovsky, Christine Allen-Blanchette and Ye Zhu
International Conference on Machine Learning (ICMLW) Sampling and Optimization in Discrete Space Workshop, 2023.
ICON^2: Reliably Benchmarking Predictive Inequity in Object Detection
Sruthi Sudhakar, Viraj Prabhu, Olga Russakovsky and Judy Hoffman
arXiv preprint arXiv:2306.04482, 2023.
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong and Andres Monroy-Hernandez
ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2023.
Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong and Olga Russakovsky
Computer Vision and Pattern Recognition (CVPR), 2023.
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong and Andres Monroy-Hernandez
ACM Conference on Human Factors in Computing Systems (CHI), 2023.
[paper] [supplement] [30-sec video] [10-min video] [bibtex]
Siri: A simple selective retraining mechanism for transformer-based visual grounding
Mengxue Qu, Yu Wu, Wu Liu, Qiqi Gong, Xiaodan Liang, Olga Russakovsky, Yao Zhao and Yunchao Wei
European Conference on Computer Vision (ECCV), 2022.
ELUDE: Generating Interpretable Explanations via a Decomposition into Labelled and Unlabelled Features
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister, Ruth Fong and Olga Russakovsky
arXiv preprint arXiv:2206.07690, 2022.
Learning Actionness from Action/Background Discrimination
Ozge Yalcinkaya Simsek, Olga Russakovsky and Pinar Duygulu
Signal, Image and Video Processing (SIViP), 2022.
Enabling Detailed Action Recognition Evaluation Through Video Dataset Augmentation
Jihoon Chung, Yu Wu and Olga Russakovsky
Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2022.
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
Zhiwei Deng and Olga Russakovsky
Neural Information Processing Systems (NeurIPS), 2022.
HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong and Olga Russakovsky
European Conference on Computer Vision (ECCV), 2022.
[paper] [code] [website] [extended abstract] [2-min video] [8-min video] [bibtex]
Multi-Query Video Retrieval
Zeyu Wang, Yu Wu, Karthik Narasimhan and Olga Russakovsky
European Conference on Computer Vision (ECCV), 2022.
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
Angelina Wang, Vikram V. Ramaswamy and Olga Russakovsky
ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022.
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
Carlos E. Jimenez, Olga Russakovsky and Karthik Narasimhan
Association for Computational Linguistics (ACL), 2022.
A Study of Face Obfuscation in ImageNet
Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng and Olga Russakovsky
International Conference on Machine Learning (ICML), 2022.
[paper] [code] [project] [bibtex]
Understanding and Evaluating Racial Biases in Image Captioning
Dora Zhao, Angelina Wang and Olga Russakovsky
International Conference on Computer Vision (ICCV), 2021.
[paper] [code] [website] [bibtex]
Point and Ask: Incorporating Pointing into Visual Question Answering
Arjun Mani, Nobline Yoo, Will Hinthorn and Olga Russakovsky
Computer Vision and Pattern Recognition (CVPRW) Visual Question Answering Workshop, 2021.
[paper] [code] [website] [bibtex]
Directional Bias Amplification
Angelina Wang and Olga Russakovsky
International Conference on Machine Learning (ICML), 2021.
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Sunnie S. Y. Kim, Sharon Zhang, Nicole Meister and Olga Russakovsky
ML Reproducibility Challenge, 2020.
ReScience C, 2021.
Fair Attribute Classification through Latent Space De-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim and Olga Russakovsky
Computer Vision and Pattern Recognition (CVPR), 2021.
[paper] [code] [website] [bibtex]
Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation
Zhiwei Deng, Karthik Narasimhan and Olga Russakovsky
Neural Information Processing Systems (NeurIPS), 2020.
CornerNet-Lite: Efficient Keypoint Based Object Detection
Hei Law, Yun Teng, Olga Russakovsky and Jia Deng
British Machine Vision Conference (BMVC), 2020.
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Angelina Wang, Arvind Narayanan and Olga Russakovsky
European Conference on Computer Vision (ECCV), 2020.
[paper] [code] [video] [90-sec video] [10-min video] [bibtex]
Towards Unique and Informative Captioning of Images
Zeyu Wang, Berthy Feng, Karthik Narasimhan and Olga Russakovsky
European Conference on Computer Vision (ECCV), 2020.
[paper] [code] [1-min video] [10-min video] [bibtex]
Take The Scenic Route: Improving Generalization In Vision-and-language Navigation
Felix Yu, Zhiwei Deng, Karthik Narasimhan and Olga Russakovsky
Computer Vision and Pattern Recognition (CVPRW) Visual Learning with Limited Labels Workshop, 2020.
[paper] [code] [video] [bibtex]
Towards Fairness In Visual Recognition: Effective Strategies For Bias Mitigation
Zeyu Wang, Klint Qinami, Ioannis C. Karakozis, Kyle Genova, Prem Nair, Kenji Hata and Olga Russakovsky
Computer Vision and Pattern Recognition (CVPR), 2020.
[paper] [code] [1-min video] [bibtex]
Towards Fairer Datasets: Filtering And Balancing The Distribution Of The People Subtree In The Imagenet Hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng and Olga Russakovsky
Conference on Fairness, Accountability and Transparency (FAT*), 2020.
[paper] [project] [Wired article] [bibtex]
Spatialsense: An adversarially crowdsourced benchmark for spatial relation recognition
Kaiyu Yang, Olga Russakovsky and Jia Deng
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.
Compositional Temporal Visual Grounding of Natural Language Event Descriptions
Jonathan Stroud, Ryan McCaffrey, Rada Mihalcea, Jia Deng and Olga Russakovsky
arxiv preprint arXiv:1912.02256, 2019.
Human Uncertainty Makes Classification More Robust
Joshua C. Peterson*, Ruairidh M. Battleday*, Thomas L. Griffiths and Olga Russakovsky
(* = equal contribution)
International Conference on Computer Vision (ICCV), 2019.
An Adversarially Crowdsourced Benchmark For Spatial Relation Recognition
Kaiyu Yang, Olga Russakovsky and Jia Deng
International Conference on Computer Vision (ICCV), 2019.
The more you look, the more you see: towards general object understanding through recursive refinement
Jingyan Wang, Olga Russakovsky and Deva Ramanan
Winter Conference on Applications of Computer Vision (WACV), 2018.
[paper] [code] [supplement] [bibtex]
What Actions Are Needed For Understanding Human Actions In Videos?
Gunnar Sigurdsson, Olga Russakovsky and Abhinav Gupta
International Conference on Computer Vision (ICCV), 2017.
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori and Li Fei-Fei
International Journal of Computer Vision (IJCV), 2017.
What's in a Question: Using Visual Questions as a Form of Supervision
Siddha Ganju, Olga Russakovsky and Abhinav Gupta
Computer Vision and Pattern Recognition (CVPR), 2017.
Predictive-Corrective Networks for Action Detection
Achal Dave, Olga Russakovsky and Deva Ramanan
Computer Vision and Pattern Recognition (CVPR), 2017.
Learning to Learn from Noisy Web Videos
Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori and Li Fei-Fei
Computer Vision and Pattern Recognition (CVPR), 2017.
Crowdsourcing in Computer Vision
Adriana Kovashka, Olga Russakovsky, Li Fei-Fei and Kristen Grauman
Foundation and Trends in Computer Vision and Graphics, 2016.
Much Ado About Time: Exhaustive Annotation of Temporal Data
Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev and Abhinav Gupta
Conference on Human Computation and Crowdsourcing (HCOMP), 2016.
[paper] [project] [poster] [slides key] [slides pdf] [bibtex]
What's the Point: Semantic Segmentation with Point Supervision
Amy Bearman, Olga Russakovsky, Vittorio Ferrari and Li Fei-Fei
European Conference on Computer Vision (ECCV), 2016.
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori and Li Fei-Fei
Computer Vision and Pattern Recognition (CVPR), 2016.
Towards More Gender Diversity in CS through an Artificial Intelligence Summer Program for High School Girls
Marie E. Vachovsky, Grace Wu, Sorathan Chaturapruek, Olga Russakovsky, Rick Sommer and Li Fei-Fei
Special Interest Group on Computer Science Education (SIGCSE), 2016.
[paper] [SAILORS camp homepage] [Wired article] [bibtex]
Scaling up Object Detection
Olga Russakovsky
PhD Thesis, Stanford University, 2015.
Best of both worlds: human-machine collaboration for object annotation
Olga Russakovsky, Li-Jia Li and Li Fei-Fei
Computer Vision and Pattern Recognition (CVPR), 2015.
Joint calibration of Ensemble of Exemplar SVMs
Davide Modolo, Alexander Vezhnevets, Olga Russakovsky and Vittorio Ferrari
Computer Vision and Pattern Recognition (CVPR), 2015.
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg and Li Fei-Fei
(* = equal contribution)
International Journal of Computer Vision (IJCV), 2015.
[paper] [paper content on arxiv] [browse detection data] [attribute annotations] [ILSVRC homepage] [bibtex]
Scalable Multi-Label Annotation
Jia Deng, Olga Russakovsky, Jonathan Krause, Michael Bernstein, Alexander Berg and Li Fei-Fei
ACM Conference on Human Factors in Computing Systems (CHI), 2014.
Detecting avocados to zucchinis: what have we done, and where are we going?
Olga Russakovsky, Jia Deng, Zhiheng Huang, Alexander Berg and Li Fei-Fei
International Conference on Computer Vision (ICCV), 2013.
[paper] [supplement] [additional analysis] [attribute annotations] [poster] [poster of talk at BAVM] [slides pptx] [slides pdf] [bibtex]
Object-centric spatial pooling for image classification
Olga Russakovsky, Yuanqing Lin, Kai Yu and Li Fei-Fei
European Conference on Computer Vision (ECCV), 2012.
[paper] [poster] [slides pptx] [slides pdf] [30-sec spotlight] [FAQ] [bibtex]
Attribute learning in large-scale data
Olga Russakovsky and Li Fei-Fei
European Conference on Computer Vision (ECCVW) Parts and Attributes Workshop, 2010.
[paper] [slides odp] [slides pdf] [data] [bibtex]
A Steiner tree approach to efficient object detection
Olga Russakovsky and Andrew Y. Ng
Computer Vision and Pattern Recognition (CVPR), 2010.
[paper] [poster] [data] [bibtex]
Autonomous operation of novel elevators for robot navigation
Ellen Klingbeil, Blake Carpenter, Olga Russakovsky and Andrew Y. Ng
International Conference on Robotics and Automation (ICRA), 2010.
STanford AI Robot (STAIR) Vision Library
Stephen Gould, Olga Russakovsky, Ian Goodfellow, Paul Baumstarck, Andrew Y. Ng and Daphne Koller
http://ai.stanford.edu/~sgould/svl, 2010.